Overall planning of aero-engine assembly based on improved flower pollination algorithm
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摘要:
针对航空发动机结构复杂、零件数量多且装配效率低、装配成本高的问题，提出了一种改进花授粉算法（improved flower pollination algorithm, IFPA）的装配顺序优化方法。以装配优先性、装配稳定性、装配聚合性、装配重定向性和基础部件位置为影响因子构建优化目标评价体系，采用了不同的表示方案、反对立学习的初始种群生成、动态调整的转换概率，在全局授粉和局部授粉规则中引入了均匀变异和精英变异，并加入遗传突变。运用在航空发动机低压压气机装配规划上，验证了IFPA的有效性，并讨论了IFPA的参数影响，并同粒子群算法、遗传算法、蚁群算法和花授粉算法进行比较，该算法找到最优序列的概率分别提高了41%、42%、41%和20%，验证了IFPA在求解装配序列规划问题上的优越性。
Abstract:In view of the problems of complex structure, large number of parts, low assembly efficiency and high assembly cost of aero-engine, an assembly sequence optimization method based on improved flower pollination algorithm (IFPA) was proposed. The optimization target evaluation system was constructed with the influence factors of assembly priority, assembly stability, assembly aggregation, assembly redirection and basic component position. Different representation schemes, initial population generation against independent learning, and dynamically adjusted transition probability were adopted, uniform and elite variation was introduced in global and local pollination rules, and genetic mutation was added. The effectiveness of IFPA was verified by applying it to the assembly planning of aero-engine low-pressure compressor, and the parameter influence of IFPA was discussed. And compared with particle swarm algorithm, genetic algorithm, ant colony algorithm and flower pollination algorithm, the probability of finding the optimal sequence increased by 41%, 42%, 41% and 20%, respectively, which verified that IFPA can solve the assembly sequence planning superiority in question.
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图 7 5种算法平均适应度值收敛图
Figure 7. Convergence graph of average fitness value of 5 algorithms
图 8 5种算法最优适应度值收敛图
Figure 8. Convergence graph of optimal fitness value of 5 algorithms
表 1 低压压气机装配信息
Table 1. Low pressure compressor assembly information
编号 零件名称 装配工具 方向 1 整流罩 T3 +z 2 螺母 T4 +z 3 第1级压气机盘 T3 +z 4 花键螺栓 T3 +z 5 第2级压气机盘 T1 +z 6 间隔衬套 T1 +z 7 第3级压气机盘 T1 +z 8 前涨圈座 T2 +z 9 前轴承 T2 +z 10 前间隔衬套 T2 +z 11 主动齿轮 T2 +z 12 后涨圈座 T2 +z 13 低压压气机轴 T1 −z 14 中介支撑轴承螺帽 T4 −z 15 涨圈座 T3 −z 16 前中介轴承 T3 −z 17 中介轴承衬套 T3 −z 18 螺母 T4 −z 
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表 2 不同参数对IFPA的影响
Table 2. Effect of different parameters on IFPA
种群数 步长 平均值 次数 最优值 10 1 9.5025 1 8.85 9 9.495 1 8.85 18 9.495 2 8.85 20 1 9.435 4 8.85 9 9.4175 5 8.85 18 9.39 7 8.85 50 1 9.32 12 8.85 9 9.3275 11 8.85 18 9.285 16 8.85 80 1 9.23 24 8.85 9 9.15 20 8.85 18 9.175 20 8.85 150 1 9.12 31 8.85 9 9.075 37 8.85 18 9.0775 33 8.85 200 1 9.09 39 8.85 9 9.04 43 8.35 18 9.0675 40 8.85
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表 3 5种算法100次对比结果
Table 3. 100 comparison results of 5 algorithms
参数 GA PSO ACO FPA IFPA 最优适应度值 13.75 9.1 13.35 8.85 8.35 最优适应度值平均值 18.95 10.96 18.31 9.23 9.04 最优序列次数 1 2 1 23 43 获最优的概率/% 1 2 1 23 43
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表 4 5种算法获得的压气机最佳序列比较
Table 4. Comparison of compressor optimal sequences obtained by five algorithms
算法 最优序列 最优
适应度值可行性
违规数装配稳定
性数装配工具
改变次数重定
向次数基础
部件值GA （13,14,7,6,5,15,12,11,9,8,
4,16,3,10,2,1,17,18）13.75 3 19 12 7 0 PSO （13,14,15,16,7,6,5,12,17,11,
18,10,9,8,4,3,2,1）9.1 0 20 10 4 0 ACO （13,14,16,6,7,15,5,17,11,18,
12,10,9,8,4,3,2,1）13.35 3 20 12 7 0 FPA （13,7,6,4,5,12,14,15,16,11,
17,18,10,9,8,3,2,1）8.85 0 20 9 4 0 IFPA （13,14,15,16,17,7,4,6,5,18,
12,11,10,9,8,3,2,1）8.35 0 20 7 4 0
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